Recent models perform joint graph learning and forecasting in multivariate timeseries. These models are GTS (“graph for timeseries”) [1]}, Graph Deviation Network (GDN) [2]}, MTS forecasting with GNNs (MTGNN) [3]}, and Neural Relational Inference (NRI) [4]}. Here, we briefly introduce these four methods and their differences and commonalities; for a more detailed overview, see Appendix .
